Triple
T3752194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Purple Line (CTA) |
E81356
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Clark/Lake |
E102527
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Clark/Lake | Statement: [Purple Line (CTA), hasStation, Clark/Lake]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Clark/Lake Context triple: [Purple Line (CTA), hasStation, Clark/Lake]
-
A.
Clark/Lake
chosen
Clark/Lake is a major Chicago 'L' rapid transit station in the Loop that serves multiple CTA lines and functions as a key downtown transfer hub.
-
B.
Clark
Clark is the middle name of Herbert Hoover, the 31st president of the United States.
-
C.
Clark
Clark is a common English-language surname borne by numerous notable individuals across fields such as literature, politics, science, and entertainment.
-
D.
City of Lakes
City of Lakes is a popular nickname for Udaipur, a picturesque city in Rajasthan, India, renowned for its numerous interconnected lakes and romantic waterfront scenery.
-
E.
City of Lakes
City of Lakes is a popular nickname for Minneapolis, highlighting its many urban lakes and waterfronts.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ad8b19b7b08190a6188804e99c53e9 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcb92135c819093f6d616d3ad28ff |
completed | March 8, 2026, 7:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4db35fa5c81909b26b96cd70ebef8 |
completed | March 14, 2026, 3:51 a.m. |
Created at: March 8, 2026, 3:35 p.m.